How to Use Compensation Benchmarking Data to Enhance Your Compensation Strategy

Creating a fair, competitive, and effective compensation program is a cornerstone for attracting and retaining top talent. To help you navigate this complex process, we’ve put together this guide on how to use salary, equity, and other compensation data to inform your compensation strategy effectively.

Understanding Benchmarking Datasets

Compensation datasets provide valuable insights into compensation trends, but they come with their own set of challenges. Here are three crucial points to keep in mind:

1. Dataset Imperfections: Datasets are samples and can be sensitive to the number of data points and proper job matching. They are not perfect, so treat the data as informative rather than definitive
2. Proper Interpretation: Use the data to guide your decisions, not dictate them. It’s essential to balance the benchmarking data with your organization’s unique needs and contexts
3. Specificity vs. Accuracy: While increasing specificity by applying more filters might seem beneficial, it often reduces the number of data points and can lower accuracy. Carefully navigate these trade-offs

Best Practices for Using Benchmarking Data

1. Define Your Compensation Philosophy
   • Establish a clear compensation philosophy that aligns with your company’s values and goals. This philosophy will guide how you use compensation data and inform your overall compensation strategy
   • A well-defined compensation philosophy will help you make informed decisions, ensuring consistency and fairness across the organization

2. Build Your Own Compensation Bands
   • Create compensation bands tailored to your organization’s needs. This approach gives you the flexibility to adapt market data appropriately and ensures consistency in your compensation decisions
   • By using your compensation philosophy as a foundation, you can define market reference points and build bands that accommodate variations in experience, performance, and market fluctuations
3. Prioritize Dataset Requirements
   • Determine the key factors important to your organization, such as job roles, locations, and industry specifics. Use these priorities to evaluate and select the most suitable datasets
   • Few companies will find a single dataset that perfectly matches all their requirements, so trade-offs must often be made. Create a prioritized list of what is most important to your organization from a dataset
4. Utilize Multiple Datasets
   • If possible, incorporate data from multiple sources to get a more comprehensive view of the market. This helps ensure the data's reliability and completeness.
   • Using multiple datasets allows you to cross-reference information, increasing your confidence in the figures and making your compensation program more robust

Pitfalls to Avoid

1. Using Market Percentiles as Your Bands
   • Avoid directly adopting market percentiles as your salary or equity bands. This can lead to pay inequities, inconsistent decisions, and challenges in recruitment and retention
   • Market percentiles are statistical distributions and should be used as a reference point rather than a definitive guide
2. Over-Applying Filters
   • Applying too many filters can result in insufficient data, leading to incomplete or misleading results. Balance specificity with the need for a robust dataset
   • Ensure that the data points are sufficient to provide a comprehensive view of the market trends
3. Sharing Data Without Context
   • Always provide proper context when sharing compensation benchmarks. Misinterpretation of the data can erode trust and create misconceptions about compensation benchmarks
   • Properly understanding and communicating the context of the benchmarking compensation data is crucial for maintaining transparency and trust within the organization

Conclusion

Using benchmarking compensation data effectively can significantly enhance your compensation strategy. By understanding the limitations of the data, defining a clear compensation philosophy, building tailored compensation bands, prioritizing dataset requirements, and utilizing multiple datasets, you can create a compensation program that is fair, competitive, and aligned with your organization’s goals. Avoid common pitfalls by interpreting data appropriately and providing context when sharing insights.

For more detailed guidance, download our comprehensive guide on How to Use Benchmarking Data.